Alternating Direction Method of Multipliers(ADMM) Based Deconvolving Images withUnknown Boundaries

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering

article

K.Kalyani¹  K.Jansi Lakshmi¹  N.Pushpalatha¹ 

[1] Dept. of ECE, AITS

关键词: Image deconvolution;    alternating direction method of multipliers (ADMM);    boundary conditions;    periodic deconvolution;    inpainting;    frames.;   

DOI  :  10.15662/ijareeie.2014.0310035

来源: Research & Reviews

【 摘 要 】

Deconvolution is an ill-posed inverse problem, it can be solvedby imposing some form of regularization (prior knowledge) on the unknown blur and original image.This formulation allows frame-based regularization. In several imaging inverse problems, ADMM is an efficient optimization tool that achieves state-of-the-art speed, by splitting the underlying problem into simpler, efficiently solvable sub-problems. In dconvolution the observation operator is circulant under periodic boundary conditions, one of these sub-problems requires a matrix inversion, which can be efficiently computable(via the FFT). we show that the resulting algorithms inherit the convergence guarantees of ADMM. These methods are experimentally illustrated using frame-based regularization; the results show the advantage of our approach over the use of the ―edgetaper‖ function (in terms of improvement in SNR).

【 授权许可】

Unknown